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Real-time data streams classification is a challenging data mining task. In real-time streaming environments concepts of instances might change at any time such as weather predictions, astronomical and intrusion detection etc. To address this issue, we present an adaptive ensemble classifier for data streams classification, which uses a set of decision trees for mining complex noisy instances in data...
Machine learning can extract desired knowledge and ease the development bottleneck in building expert systems. Among the proposed approaches, deriving classification rules from training examples is the most common. Given a set of examples, a learning program tries to induce rules that describe each class. The rough-set theory has served as a good mathematical tool for dealing with data classification...
After having analyzed the factors of the mine flood of the coal floor, studied the theory of coal floor water bursting, and established the model library of mine flood forecast system, we finally designed the coal floor water bursting forecast system and make it to appliance from the surface to the point, with the help of GIS(Geographic Information System) spatial analysis tools and database management...
By using the remote functions of a modern IT service management system infrastructure, it is possible to analyze huge amounts of log file data from complex technical equipment. This enables a service provider to predict failures of connected equipment before they happen. The problem most providers face in this context is finding "a needle in a haystack" - the obtained amount of data turns...
The CHRONIOUS system addresses a smart wearable platform, based on multi-parametric sensor data processing, for monitoring people suffering from chronic diseases in long-stay setting. Several signals are being recorded through wearable sensors and are stored together with additional information, entered by the patient. An Intelligent System, placed at a Smart Assistant Device, analyzes incoming data...
With the rapid growth in the credit industry, credit scoring classifiers are being widely used for credit admission evaluation. Effective classifiers have been regarded as a critical topic, with the related departments striving to collect huge amounts of data to avoid making the wrong decision. Finding effective classifier is important because it will help people make an objective decision instead...
Case retrieval is the most crucial part in CBR. However, traditional case retrieval methods have many disadvantages on accuracy and efficiency. In order to cope with this problem, an improving method based on self-organizing maps (SOM) was proposed in this paper. Firstly, cluster previous cases into several groups using of SOM networks; secondly, input the new case into SOM networks, and identify...
An artificial neural network (ANN) model is established to recognize the drilled formations' lithologies while drilling. The styles of output and input of ANN are designed. The nerve cells in input layer are weight of bit (WOB), speed of rotary (SOR) and rate of penetration (ROP). The number of nerve cells in output layer is designed to be three. Software system for recognizing the formation lithologies...
The prediction of financial time series is a very complicated process. An initial look at financial time series gives the impression that they are random in nature. If true, this would make the forecast, and therefore the trading, of such series exceptionally difficult. The efficient market hypothesis states that the current price contains all available information in the market. This leads to the...
The development of credit scoring model has been regarded as a critical topic. This study proposed four approaches combining with the KNN (K-nearest neighbor) classifier for features selection that retains sufficient information for classification purpose. Two UCI data sets and different models combined with KNN classifier were constructed by selecting features. KNN classifier combines with conventional...
Learning under imbalanced dataset can be difficult since traditional algorithms are biased towards the majority class, providing low predictive accuracy over the minority one. Among the several methods proposed in the literature to overcome such a limitation, the most recent uses multi-experts system (MES) composed of balanced classifiers, whose decisions are aggregated according to a combination...
The aim of this paper is to detect incoherences in concepts, ideas, values, and others contained in technical document corpora. The way in which document collections are generated, modified or updated generates problems and mistakes in the information coherency, leading to legal, economic and social problems. A solution based on summarization, matching and neuro-fuzzy systems is proposed to dealt...
In English courses, it is very important to assign proper articles to individual students for training their reading ability. This study proposes an innovative approach for developing reading material recommendation systems by eliciting domain knowledge from multiple experts. An experiment has been conducted to evaluate the performance of the approach; moreover, a comparison on the existing approaches...
Distributor selection is a crucial issue in supply chain especially in today's internationalized competitive environment. The existing research works provide only conceptual, descriptive, and simulation results, focusing mainly on firm resources and general marketing/selling factors. In this paper, the rough set theory (RST) is introduced for distributor selection with weight incorporate features...
In the practice of risk evaluation on real estate, there are many events' degree of risk can not be accurately described, the application of fuzzy comprehensive evaluation method can reflect the risk degree of every element in detail. In addition, the combination use of BP neural network (ANN) and expert system (Es) method can determine impact extent of the risk factors on the real estate risk and...
With the pressures on the cement producers to reduce the use of high cost, high-grade materials, extension of quarry lifetime and, at the same time, to maximize output while guaranteeing a constant product quality the need for analytical real-time material characterization and optimization reaches a critical stage. Only a tuned combination of an analytical on-line system with an expert system can...
Phytoplankton becomes a concern to the environment when it forms dense growth at the water surface, known as algal bloom. However, studies on mechanism of algal bloom are not straight forward mainly caused by uncertainty and complexity of alga ecosystems. This paper describes the analysis of limnological time-series of Putrajaya Lake and wetlands to determine the growth of alga based on Kohonen self...
In to function effectively leadership and various departments need to have a shared knowledge base in order to take organization preferred decisions. However internally gaps appear and as a consequence the organization's performance is impacted. In this study, we try to construct a knowledge gaps model by PZB theory. Then we adopted the simulated annealing technique in order to arrive at equilibrium...
The credit scoring has been regarded as a critical topic. Creating an effective classificatory model will objectively help managers instead of intuitive experience. This study proposed four strategies combining with the SVM (support vector machine) classifier for features selection that retains sufficient information for classification purpose. Different features preprocessing steps were constructed...
A knowledge-based inference system for weightless neural networks (WNNs) is described in this paper. With the use such a system, rules can be inserted and extracted into/from WNNs. The process of rule insertion and rule extraction in WNNs is often more natural than in other neural network models. The system proposed allows the understanding on how the neural networks reach the solution to a problem...
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